Comparison of a genetic algorithm and mathematical programming to the design of groundwater cleanup systems

We present and apply a new simulation/optimization approach for single- and multiple-planning period problems in groundwater remediation. Instead of the traditional control locations for contaminant concentrations, we use an LQC norm as a global measure of aquifer contamination (CMAX). We use response-surface constraints to represent CMAX within the optimization model. We compare the performance of formal mixed integer nonlinear programming and a genetic algorithm for several optimization scenarios